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Learning with noisy labels is one of the hottest problems in weakly-supervised learning.
Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T · 1998
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Semi-supervised learning
Chapelle, O., Scholkopf, B., and Zien, A · 2009
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On the design of loss functions for classification: theory, robustness to outliers, and savageboost
Masnadi-Shirazi, H. and Vasconcelos, N · 2009
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High-frequency covariance estimates with noisy and asynchronous financial data
Aït-Sahalia, Y., Fan, J., and Xiu, D · 2010
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Learning from crowds
Raykar, V., Yu, S., Zhao, L., Valadez, G., Florin, C., Bogoni, L., and Moy, L · 2010
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The multidimensional wisdom of crowds
Welinder, P., Branson, S., Perona, P., and Belongie, S · 2010
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Noise resistant graph ranking for improved web image search
Liu, W., Jiang, Y., Luo, J., and Chang, S · 2011
Earlier work this paper cites.
Learning with noisy labels
Natarajan, N., Dhillon, I., Ravikumar, P., and Tewari, A · 2013
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Evolving culture versus local minima
Bengio, Y · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C · 2014
Earlier work this paper cites.
Class proportion estimation with application to multiclass anomaly rejection
Sanderson, T. and Scott, C · 2014
Earlier work this paper cites.
Learning from multiple annotators with varying expertise
Yan, Y., Rosales, R., Fung, G., Subramanian, R., and Dy, J · 2014
Earlier work this paper cites.
Learning from corrupted binary labels via class-probability estimation
Menon, A., Van Rooyen, B., Ong, C., and Williamson, B · 2015
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Training deep neural networks on noisy labels with bootstrapping
Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., and Rabinovich, A · 2015
Cited alongside, same era.
Learning with symmetric label noise: The importance of being unhinged
van Rooyen, B., Menon, A., and Williamson, B · 2015
Cited alongside, same era.
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2016
Cited alongside, same era.
Virtual adversarial training for semi-supervised text classification
Miyato, T., Dai, A., and Goodfellow, I · 2016
Cited alongside, same era.
Theoretical foundation of co-training and disagreement-based algorithms
Wang, W. and Zhou, Z.-H · 2017
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
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Training a neural network based on unreliable human annotation of medical images
Dgani, Y., Greenspan, H., and Goldberger, J · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise
Hendrycks, D., Mazeika, M., Wilson, D., and Gimpel, K · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Jiang, L., Zhou, Z., Leung, T., Li, L., and Fei-Fei, L · 2018
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A closer look at memorization in deep networks
Arpit, D., Jastrzebski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M., Maharaj, T., Fischer, A., Courville, A., and Bengio, Y · 2017
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer
Goldberger, J. and Ben-Reuven, E · 2017
Cited alongside, same era.
Positive-unlabeled learning with non-negative risk estimator
Kiryo, R., Niu, G., Du Plessis, M., and Sugiyama, M · 2017
Cited alongside, same era.
Learning from noisy labels with distillation
Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., and Li, J · 2017
Cited alongside, same era.
Decoupling” when to update” from” how to update”
Malach, E. and Shalev-Shwartz, S · 2017
Cited alongside, same era.
Making deep neural networks robust to label noise: A loss correction approach
Patrini, G., Rozza, A., Menon, A., Nock, R., and Qu, L · 2017
Cited alongside, same era.
Dimensionality-driven learning with noisy labels
Ma, X., Wang, Y., Houle, M., Zhou, S., Erfani, S., Xia, S., Wijewickrema, S., and Bailey, J · 2018
Later among the works it cites.
Learning to reweight examples for robust deep learning
Ren, M., Zeng, W., Yang, B., and Urtasun, R · 2018
Later among the works it cites.
Deep learning from crowds
Rodrigues, F. and Pereira, F · 2018
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Joint optimization framework for learning with noisy labels
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K · 2018
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Iterative learning with open-set noisy labels
Wang, Y., Liu, W., Ma, X., Bailey, J., Zha, H., Song, L., and Xia, S · 2018
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An efficient and provable approach for mixture proportion estimation using linear independence assumption
Yu, X., Liu, T., Gong, M., Batmanghelich, K., and Tao, D · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M · 2018
Later among the works it cites.
Robust inference via generative classifiers for handling noisy labels
Lee, K., Yun, S., Lee, K., Lee, H., Li, B., and Shin, J · 2019
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